Software bots have attracted increasing interest and popularity in both research and society. Their contributions span automation, digital twins, game characters with conscious-like behavior, and social media. However, there is still a lack of intelligent bots that can adapt to the variability and dynamic nature of digital web environments. Unlike human users, they have difficulty understanding and exploiting the affordances across multiple virtual environments. Despite the hype, bots with human user-like cognition do not currently exist. Chatbots, for instance, lack situational awareness on the digital platforms where they operate, preventing them from enacting meaningful and autonomous intelligent behavior similar to human users. In this survey, we aim to explore the role of cognitive architectures in supporting efforts towards engineering software bots with advanced general intelligence. We discuss how cognitive architectures can contribute to creating intelligent software bots. Furthermore, we highlight key architectural recommendations for the future development of autonomous, user-like cognitive bots.

The way we think about AI is shaped by works of science-fiction. In the big picture, fiction provides the conceptual building blocks we use to make sense of the long-term significance of "thinking machines" for our civilization and even our species. Zooming in, fiction provides the familiar narrative frame leveraged by the media coverage of new AI-powered product releases. As a result, the dominant view in the popular imagination today is that AI is about creating artificial minds, agents with a will of their own. These agents, since they possess a similar kind of autonomy as their human creators, may decide to pursue their own goals, and eventually turn against humans.

The foundation of cognitive automation is software that adds intelligence to information-intensive processes. It is frequently referred to as the union of cognitive computing and robotic process automation (RPA), or AI. By utilizing AI technology, cognitive automation broadens and enhances the set of tasks normally associated with RPA, resulting in cost savings, increased customer satisfaction, and increased accuracy in intricate business processes involving unstructured data. Various combinations of artificial intelligence (AI) with process automation capabilities are referred to as cognitive automation to improve business outcomes. Cognitive automation represents a range of strategies that enhance automation's ability to gather data, make decisions, and scale automation. It also suggests how AI and automation capabilities may be packaged for best practices documentation, reuse, or inclusion in an app store for AI services. Machine learning is not cognitive automation.

Cognitive automation is an emerging new field of machine learning tools, that aims to do exactly that - to automate tasks that ... source

The concept of automation in business and non-business functions has undergone more than a few evolutions along the way. The earliest types of automation-related applications could only carry out repetitive tasks such as printing and basic calculations. In a bid to save time and minimize human error, such applications were used by businesses and individuals to automate the tasks that, according to organizations, employees didn't need to waste their energy on. The eventually widespread adoption of IoT, AI and robotics resulted in the growth of cognitive automation to execute more challenging, diverse and multifaceted functions such as supply chain operations, robotic surgery, architecture and construction. The sheer accuracy and consistency of cognitive automation tools powered by AI and robotics allow organizations to evaluate data at lightning-quick speed, predict future trends in consumer demand patterns and formulate robust strategies and frameworks for improved operational efficiency and regulatory compliance.

AI can easily identify background and mask it out thanks to image segmentation. AI driven recommendations is another classic problem where otherwise people had to put together excel sheets of similar products / complementary products. Some ecommerce brands on shopify and magento platform create and upload these product bundles manually to this day! They can surely get benefitted by such tech. There are extremely complex tasks like autonomous driving which can also be defined as cognitive automation. A driver is taking bunch of known decisions like controlling the speed and direction of the vehicle based on visual input from roads. In case of autonomous driving, the variables are quite large and hence the AI models are going to be lot more complex. But, smaller companies in other industries can greatly benefit from the same technology to provide immediate value to their customers.

AI is the perfect complement to RPA, together providing more accurate and efficient automation powered by an informed knowledge base. AI is the process behind the effort to simulate human intelligence in machines, while RPA automates processes that use structured data and logic. Intelligent automation (IA) is the combination of AI and automation technologies, such as cognitive automation, machine learning, business process automation (BPA) and RPA. This simplification enables the user to think about the outcome or goal rather than the process used to get that result or the boundaries between applications. The use of intelligent tools, such as virtual assistants and chatbots, equips organizations with key insights that help in automation efficiency and faster response to customers.

RPA and cognitive automation are sometimes used interchangeably. While they are both important technologies, there are some fundamental differences in how they work, what they can do and how CIOs need to plan for their implementation within their organization. Key distinctions between robotic process automation (RPA) vs. cognitive automation include how they complement human workers, the types of data they work with, the timeline for projects and how they are programmed. CIOs also need to address different considerations when working with each of the technologies. RPA is typically programmed upfront but can break when the applications it works with change.

Businesses across the globe are going through rapid digital transformation and automation. Cutting-edge technologies like AI, robotics, and IoT are enabling this transformation by enhancing business efficiency and agility. Robotic Process Automation (RPA) and Cognitive Automation are two components of redefining and automating industry-wide business operations. According to a Statista report, the expenditure on Cognitive Robotic Process Automation is expected to reach about 3.62 billion USD globally at a CAGR of 60.9% from 2017 to 2026. Robotic Process Automation (RPA) enables the automation of mundane and repetitive tasks in an organization with maximum accuracy and minimum labour.

The supply chain is an ecosystem that affects businesses around the world, and the COVID-19 pandemic has thrown a monkey wrench into this previously undisturbed process. With region-specific restrictions, limited supply of certain goods, and a constantly changing consumer mindset, almost all businesses are playing catch up in addressing the needs of every consumer. Add to that the oil price war and the result is near chaos for both consumers and businesses. It may be a gamble to implement new supply chain systems in these circumstances, but it's a bet that could pay dividends not just now but in the long term. Artificial intelligence (AI) and data analytics tools can provide the much-needed push companies need to keep their businesses afloat--and maybe even thrive--despite the global crisis.